Osteoarthritis (OA) is the most common form of arthritis, and its prevalence is expected to further increase as our society ages.Despite many approaches to cure OA, no drugs are currently proven to modulate the progression of OA. Nowadays, new OA treatment options are holistically developed and one of the approaches of treatment option is botanical drugs. Some botanical drugs for OA have shown both therapeutic effect comparable to refined drugs in small studies and fewer side effects. Hence, there are various health functional foods which are known to relieve symptoms of OA. However, since there are many botanical products, clinicians are not familiar to the efficacy of each botanical product, making it challenging to use them appropriately in clinical practice. Here, we summarize the botanical products available for treating OA, including prescription botanical drugs and health functional foods available in Korea. Further studies and the purification of effective molecules from botanical products will be necessary in future.

Osteoarthritis is the most common form of chronic inflammatory arthritis, and its prevalence is steadily increasing owing to its association with aging. Therefore, understanding and implementing appropriate treatments for osteoarthritis in clinical practice is becoming increasingly important. Additionally, there is active research on a new approach for treating osteoarthritis: disease-modifying osteoarthritis drugs (DMOADs).Current Concepts: Several global osteoarthritis treatment guidelines exist; this article introduces the guidelines of the American College of Rheumatology and Osteoarthritis Research Society International, which are among the most widely recognized. A common theme across various guidelines is that exercise and weight loss are the primary recommended treatments. As for pharmacotherapy, the top recommendations include topical nonsteroidal anti-inflammatory drugs (NSAIDs) and oral NSAIDs. Although multiple classes of drugs such as DMOADs are being actively researched to slow the progression of osteoarthritis, no drug has yet been confirmed to be clinically effective or approved for use.Discussion and Conclusion: A deeper understanding of osteoarthritis treatment can help prevent malpractice and improve patient outcomes. While current treatments focus primarily on symptom management, the development of effective DMOADs holds promise for fundamentally altering the disease course and improving joint function and quality of life.

Osteoarthritis (OA) is the most prevalent chronic joint disorder that leads to pain, disability, and functional impairment. The OA prevalence among those aged ≥65 years in Korea was 30.2% between 2017 and 2021. Owing to a rapidly aging population, OA is becoming a significant public health and socioeconomic concern.Current Concepts: The onset of OA is characterized by cartilage injury, which affects the surrounding tissues and progresses to joint destruction. The pathophysiology varies across joints; hand OA is associated with genetic factors, whereas knee and hip OA are associated with mechanical stress. Nonsteroidal anti-inflammatory drugs are the primary treatment; however, considering their adverse effects and limited efficacy in halting disease progression, further research is warranted for more effective therapies. Natural medicines have been investigated; however, their clinical efficacy is inadequate.Discussion and Conclusion: Currently, no disease-modifying treatments are validated for OA. Animal models fail to reflect the slow progression and complexity of human OA; therefore, drug development remains challenging. In view of increasing healthcare costs and rapid population aging, further research and broader socioeconomic strategies are essential to manage the growing burden of OA.

The statistical significance of a clinical trial analysis result is determined by a mathematical calculation and probability based on null hypothesis significance testing. However, statistical significance does not always align with meaningful clinical effects; thus, assigning clinical relevance to statistical significance is unreasonable. A statistical result incorporating a clinically meaningful difference is a better approach to present statistical significance. Thus, the minimal clinically important difference (MCID), which requires integrating minimum clinically relevant changes from the early stages of research design, has been introduced. As a follow-up to the previous statistical round article on P values, confidence intervals, and effect sizes, in this article, we present hands-on examples of MCID and various effect sizes and discuss the terms statistical significance and clinical relevance, including cautions regarding their use.

Background/Aims: Hyperuricemia plays an essential role in the gout. Despite the clinical significance of hyperuricemia, a direct relationship between oral health and hyperuricemia has not been established. We aim to investigate the association between oral health and hyperuricemia. Methods: We selected 17,557 subjects from the Korea National Health and Nutrition Examination Survey database for the years 2016–2019. Oral health-related variables included the number of dental caries, regular tooth brushing, use of secondary oral products, and regular dental examinations. The odds ratio (OR) and 95% confidence intervals (CIs) for hyperuricemia were calculated using a multivariable-adjusted logistic regression model. Results: Oral health status with dental caries and oral health behaviors, including tooth brushing, secondary oral products, and regular dental examination, were significantly associated with hyperuricemia in all participants. The adjusted OR and 95% CIs for hyperuricemia comparing more than three dental caries with no dental caries were 1.28 (1.08–1.52). The adjusted OR and 95% CIs for hyperuricemia in regular tooth brushing, use of secondary oral products, and regular dental examination were 0.78 (0.67–0.91), 0.91 (0.83–1.00), and 0.86 (0.78–0.95), respectively. Notably, the association between oral health and hyperuricemia was more prominent in male subjects. In addition, when subjects were grouped by the oral health scoring system, the prevalence of hyperuricemia was lower in groups with better oral health scores. Conclusions We demonstrated that oral health status and behaviors are associated with hyperuricemia, particularly in males. Further studies are necessary to confirm the association between oral health and hyperuricemia.

Purpose: Appropriate preclinical mouse models are needed to evaluate the response to immunotherapeutic agents. Immunocompetent mouse models have rarely been reported for gastric cancer. Thus, we investigated immunophenotypes and responses to immune checkpoint inhibitor (ICI) in immunocompetent mouse models using various murine gastric cancer cell lines. Materials and Methods: We constructed subcutaneous syngeneic tumors with murine gastric cancer cell lines, YTN3 and YTN16, in C57BL/6J mice. Mice were intraperitoneally treated with IgG isotype control or an anti–programmed death-ligand 1 (PD-L1) neutralizing antibody. We used immunohistochemistry to evaluate the tumor-infiltrating immune cells of formalin-fixed paraffin-embedded mouse tumor tissues. We compared the protein and RNA expression between YTN3 and YTN16 cell lines using a mouse cytokine array and RNA sequencing. Results: The mouse tumors revealed distinct histological and molecular characteristics. YTN16 cells showed upregulation of genes and proteins related to immunosuppression, such as Ccl2 (CCL2) and Csf1 (M-CSF). Macrophages and exhausted T cells were more enriched in YTN16 tumors than in YTN3 tumors. Several YTN3 tumors were completely regressed by the PD-L1 inhibitor, whereas YTN16 tumors were unaffected. Although treatment with a PD-L1 inhibitor increased infiltration of T cells in both the tumors, the proportion of exhausted immune cells did not decrease in the non-responder group. Conclusion We confirmed the histological and molecular features of cancer cells with various responses to ICI. Our models can be used in preclinical research on ICI resistance mechanisms to enhance clinical efficacy.

Tables and figures are commonly adopted methods for presenting specific data or statistical analysis results. Figures can be used to display characteristics and distributions of data, allowing for intuitive understanding through visualization and thus making it easier to interpret the statistical results. To maximize the positive aspects of figure presentation and increase the accuracy of the content, in this article, the authors will describe how to choose an appropriate figure type and the necessary components to include. Additionally, this article includes examples of figures that are commonly used in research and their essential components using virtual data.

Background: Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed. Objective: To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF. Methods: In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model. Results: In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set. Conclusions Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.